{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "H:\\Anaconda\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "print('Tensorflow version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = './dataset/2_class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = pathlib.Path(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('dataset/2_class')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset\\2_class\\airplane\n",
      "dataset\\2_class\\lake\n"
     ]
    }
   ],
   "source": [
    "for item in data_root.iterdir():\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_paths = list(data_root.glob('*/*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_count = len(all_image_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('dataset/2_class/airplane/airplane_001.jpg'),\n",
       " WindowsPath('dataset/2_class/airplane/airplane_002.jpg'),\n",
       " WindowsPath('dataset/2_class/airplane/airplane_003.jpg')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_paths[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('dataset/2_class/lake/lake_698.jpg'),\n",
       " WindowsPath('dataset/2_class/lake/lake_699.jpg'),\n",
       " WindowsPath('dataset/2_class/lake/lake_700.jpg')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_paths[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "all_image_paths = [str(path) for path in all_image_paths]\n",
    "random.shuffle(all_image_paths)\n",
    "\n",
    "image_count = len(all_image_paths)\n",
    "image_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset\\\\2_class\\\\lake\\\\lake_296.jpg',\n",
       " 'dataset\\\\2_class\\\\lake\\\\lake_496.jpg',\n",
       " 'dataset\\\\2_class\\\\lake\\\\lake_097.jpg',\n",
       " 'dataset\\\\2_class\\\\lake\\\\lake_021.jpg',\n",
       " 'dataset\\\\2_class\\\\lake\\\\lake_308.jpg']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['airplane', 'lake']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'airplane': 0, 'lake': 1}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_to_index = dict((name, index) for index,name in enumerate(label_names))\n",
    "label_to_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_labels = [label_to_index[pathlib.Path(path).parent.name] for path in all_image_paths]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython.display as display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def caption_image(label):\n",
    "    return {0: 'airplane', 1: 'lake'}.get(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for n in range(3):\n",
    "    image_index = random.choice(range(len(all_image_paths)))\n",
    "    display.display(display.Image(all_image_paths[image_index]))\n",
    "    print(caption_image(all_image_labels[image_index]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载和格式化图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dataset\\\\2_class\\\\lake\\\\lake_296.jpg'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_path = all_image_paths[0]\n",
    "img_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Tensor: id=1, shape=(), dtype=string, numpy=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x...\n"
     ]
    }
   ],
   "source": [
    "img_raw = tf.io.read_file(img_path)\n",
    "print(repr(img_raw)[:100]+\"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256, 3)\n",
      "<dtype: 'uint8'>\n"
     ]
    }
   ],
   "source": [
    "img_tensor = tf.image.decode_image(img_raw)\n",
    "\n",
    "print(img_tensor.shape)\n",
    "print(img_tensor.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256, 3)\n",
      "0.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "img_tensor = tf.cast(img_tensor, tf.float32)\n",
    "img_final = img_tensor/255.0\n",
    "print(img_final.shape)\n",
    "print(img_final.numpy().min())\n",
    "print(img_final.numpy().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_image(path):\n",
    "    image = tf.io.read_file(path)\n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    image = tf.image.resize(image, [256, 256])\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = image/255.0  # normalize to [0,1] range\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc627d8f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image_path = all_image_paths[0]\n",
    "label = all_image_labels[0]\n",
    "\n",
    "plt.imshow(load_and_preprocess_image(img_path))\n",
    "plt.grid(False)\n",
    "plt.xlabel(caption_image(label))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(all_image_labels, tf.int64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "lake\n",
      "lake\n",
      "lake\n",
      "lake\n",
      "lake\n",
      "lake\n",
      "lake\n",
      "airplane\n",
      "lake\n"
     ]
    }
   ],
   "source": [
    "for label in label_ds.take(10):\n",
    "    print(label_names[label.numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ((256, 256, 3), ()), types: (tf.float32, tf.int64)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_label_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_count = int(image_count*0.2)\n",
    "train_count = image_count - test_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = image_label_ds.skip(test_count)\n",
    "\n",
    "test_data = image_label_ds.take(test_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PrefetchDataset shapes: ((None, 256, 256, 3), (None,)), types: (tf.float32, tf.int64)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = train_data.apply(\n",
    "  tf.data.experimental.shuffle_and_repeat(buffer_size=train_count))\n",
    "train_data = train_data.batch(BATCH_SIZE)\n",
    "train_data = train_data.prefetch(buffer_size=AUTOTUNE)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.batch(BATCH_SIZE)gfc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()   #顺序模型\n",
    "model.add(tf.keras.layers.Conv2D(64, (3, 3), input_shape=(256, 256, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(512, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(512, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(1024, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
    "model.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 254, 254, 64)      1792      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 252, 252, 64)      36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 126, 126, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 124, 124, 128)     73856     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 122, 122, 128)     147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 61, 61, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 59, 59, 256)       295168    \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 57, 57, 256)       590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 26, 26, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 11, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 3, 3, 1024)        4719616   \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 10,719,562\n",
      "Trainable params: 10,719,562\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['acc']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_per_epoch = train_count//BATCH_SIZE\n",
    "validation_steps = test_count//BATCH_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "35/35 [==============================] - 797s 23s/step - loss: 1.2610 - acc: 0.4455 - val_loss: 0.6951 - val_acc: 0.5391\n",
      "Epoch 2/30\n",
      "35/35 [==============================] - 792s 23s/step - loss: 0.7011 - acc: 0.5063 - val_loss: 0.7050 - val_acc: 0.4609\n",
      "Epoch 3/30\n",
      "35/35 [==============================] - 799s 23s/step - loss: 0.5422 - acc: 0.7804 - val_loss: 0.8315 - val_acc: 0.7461\n",
      "Epoch 4/30\n",
      "35/35 [==============================] - 796s 23s/step - loss: 0.3460 - acc: 0.8232 - val_loss: 0.2776 - val_acc: 0.9258\n",
      "Epoch 5/30\n",
      "35/35 [==============================] - 795s 23s/step - loss: 0.1473 - acc: 0.9634 - val_loss: 0.1719 - val_acc: 0.9531\n",
      "Epoch 6/30\n",
      "35/35 [==============================] - 796s 23s/step - loss: 0.1354 - acc: 0.9705 - val_loss: 0.1763 - val_acc: 0.9375\n",
      "Epoch 7/30\n",
      "35/35 [==============================] - 795s 23s/step - loss: 0.1629 - acc: 0.9598 - val_loss: 0.1398 - val_acc: 0.9492\n",
      "Epoch 8/30\n",
      "35/35 [==============================] - 799s 23s/step - loss: 0.1272 - acc: 0.9634 - val_loss: 0.2340 - val_acc: 0.9492\n",
      "Epoch 9/30\n",
      "35/35 [==============================] - 797s 23s/step - loss: 0.1193 - acc: 0.9688 - val_loss: 0.1823 - val_acc: 0.9453\n",
      "Epoch 10/30\n",
      "35/35 [==============================] - 793s 23s/step - loss: 0.3207 - acc: 0.8902 - val_loss: 0.5023 - val_acc: 0.7773\n",
      "Epoch 11/30\n",
      "35/35 [==============================] - 797s 23s/step - loss: 0.2417 - acc: 0.9259 - val_loss: 0.1652 - val_acc: 0.9414\n",
      "Epoch 12/30\n",
      "35/35 [==============================] - 796s 23s/step - loss: 0.1280 - acc: 0.9554 - val_loss: 0.1653 - val_acc: 0.9336\n",
      "Epoch 13/30\n",
      "35/35 [==============================] - 791s 23s/step - loss: 0.1474 - acc: 0.9571 - val_loss: 0.1482 - val_acc: 0.9375\n",
      "Epoch 14/30\n",
      "35/35 [==============================] - 795s 23s/step - loss: 0.1033 - acc: 0.9732 - val_loss: 0.1067 - val_acc: 0.9648\n",
      "Epoch 15/30\n",
      "35/35 [==============================] - 797s 23s/step - loss: 0.0835 - acc: 0.9741 - val_loss: 0.1233 - val_acc: 0.9531\n",
      "Epoch 16/30\n",
      "35/35 [==============================] - 793s 23s/step - loss: 0.0742 - acc: 0.9759 - val_loss: 0.1262 - val_acc: 0.9648\n",
      "Epoch 17/30\n",
      "35/35 [==============================] - 792s 23s/step - loss: 0.1585 - acc: 0.9491 - val_loss: 0.1102 - val_acc: 0.9609\n",
      "Epoch 18/30\n",
      "35/35 [==============================] - 795s 23s/step - loss: 0.0957 - acc: 0.9714 - val_loss: 0.1292 - val_acc: 0.9648\n",
      "Epoch 19/30\n",
      "35/35 [==============================] - 795s 23s/step - loss: 0.0738 - acc: 0.9759 - val_loss: 0.1040 - val_acc: 0.9609\n",
      "Epoch 20/30\n",
      "35/35 [==============================] - 794s 23s/step - loss: 0.0978 - acc: 0.9777 - val_loss: 0.1512 - val_acc: 0.9609\n",
      "Epoch 21/30\n",
      "35/35 [==============================] - 797s 23s/step - loss: 0.0988 - acc: 0.9732 - val_loss: 0.1707 - val_acc: 0.9453\n",
      "Epoch 22/30\n",
      "35/35 [==============================] - 798s 23s/step - loss: 0.1420 - acc: 0.9652 - val_loss: 0.1154 - val_acc: 0.9570\n",
      "Epoch 23/30\n",
      "35/35 [==============================] - 798s 23s/step - loss: 0.2785 - acc: 0.9366 - val_loss: 0.2586 - val_acc: 0.9297\n",
      "Epoch 24/30\n",
      "35/35 [==============================] - 792s 23s/step - loss: 0.2663 - acc: 0.9393 - val_loss: 0.1833 - val_acc: 0.9297\n",
      "Epoch 25/30\n",
      "35/35 [==============================] - 800s 23s/step - loss: 0.1467 - acc: 0.9554 - val_loss: 0.1571 - val_acc: 0.9570\n",
      "Epoch 26/30\n",
      "35/35 [==============================] - 796s 23s/step - loss: 0.1174 - acc: 0.9670 - val_loss: 0.1589 - val_acc: 0.9570\n",
      "Epoch 27/30\n",
      "35/35 [==============================] - 794s 23s/step - loss: 0.0960 - acc: 0.9688 - val_loss: 0.1434 - val_acc: 0.9531\n",
      "Epoch 28/30\n",
      "35/35 [==============================] - 795s 23s/step - loss: 0.0966 - acc: 0.9741 - val_loss: 0.1135 - val_acc: 0.9688\n",
      "Epoch 29/30\n",
      "35/35 [==============================] - 794s 23s/step - loss: 0.1020 - acc: 0.9759 - val_loss: 0.4174 - val_acc: 0.8945\n",
      "Epoch 30/30\n",
      "35/35 [==============================] - 798s 23s/step - loss: 0.1245 - acc: 0.9652 - val_loss: 0.1279 - val_acc: 0.9570\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_data, epochs=30, steps_per_epoch=steps_per_epoch, validation_data=test_data, validation_steps=validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc610e70ac8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc610dee780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('acc'), label='acc')\n",
    "plt.plot(history.epoch, history.history.get('val_acc'), label='val_acc')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc610ee59e8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc610e33358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('loss'), label='loss')\n",
    "plt.plot(history.epoch, history.history.get('val_loss'), label='val_loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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